caa a22 4500 606209913 CHVBK 20210128101030.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s11220-015-0125-0 doi (NATIONALLICENCE)springer-10.1007/s11220-015-0125-0 Remote Sensing Image Fusion Method Based on Nonsubsampled Shearlet Transform and Sparse Representation [Elektronische Daten] [Altan-Ulzii Moonon, Jianwen Hu, Shutao Li] The remote sensing image fusion is an important preprocessing technique in remote sensing image processing. In this paper, a remote sensing image fusion method based on the nonsubsampled shearlet transform (NSST) with sparse representation (SR) is proposed. Firstly, the low resolution multispectral (MS) image is upsampled and color space is transformed from Red-Green-Blue (RGB) to Intensity-Hue-Saturation (IHS). Then, the high resolution panchromatic (PAN) image and intensity component of MS image are decomposed by NSST to high and low frequency coefficients. The low frequency coefficients of PAN and the intensity component are fused by the SR with the learned dictionary. The high frequency coefficients of intensity component and PAN image are fused by local energy based fusion rule. Finally, the fused result is obtained by performing inverse NSST and inverse IHS transform. The experimental results on IKONOS and QuickBird satellites demonstrate that the proposed method provides better spectral quality and superior spatial information in the fused image than other remote sensing image fusion methods both in visual effect and object evaluation. Springer Science+Business Media New York, 2015 Remote sensing image fusion nationallicence Sparse representation nationallicence Nonsubsampled shearlet transform (NSST) nationallicence High resolution panchromatic image nationallicence Low-resolution multispectral image nationallicence Moonon Altan-Ulzii College of Electrical and Information Engineering, Hunan University, 410082, Changsha, People's Republic of China aut Hu Jianwen College of Electrical and Information Engineering, Changsha University of Science and Technology, 410114, Changsha, People's Republic of China aut Li Shutao College of Electrical and Information Engineering, Hunan University, 410082, Changsha, People's Republic of China aut Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-18 1557-2064 16:1<1 2015 16 11220 https://doi.org/10.1007/s11220-015-0125-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11220-015-0125-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Moonon Altan-Ulzii College of Electrical and Information Engineering, Hunan University, 410082, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Hu Jianwen College of Electrical and Information Engineering, Changsha University of Science and Technology, 410114, Changsha, People's Republic of China aut NATIONALLICENCE 700 1- Li Shutao College of Electrical and Information Engineering, Hunan University, 410082, Changsha, People's Republic of China aut NATIONALLICENCE 773 0- Sensing and Imaging Springer US; http://www.springer-ny.com 16/1(2015-12-01), 1-18 1557-2064 16:1<1 2015 16 11220